Fine-Tuning InceptionV3 for Thai Cuisine Image Classification: A Mobile Deployment Perspective - Somsak Saksat (International Journal of Intelligent Systems and Applications in Engineering, 2025)
This work presents the development of a smartphone application that utilizes deep learning techniques for the automatic classification of Thai food images. Transfer learning and fine-tuning approaches were compared using the InceptionV3 model, initially trained on the ImageNet dataset and subsequently refined with a dataset consisting of 49 varieties of Thai cuisine images. Experimental results indicate that the fine-tuning model achieved superior performance, attaining an accuracy of 95.22% on the validation set, surpassing the transfer learning model, which achieved an accuracy of 85.43%. Additionally, the fine-tuning model exhibited a stable and consistent decrease in loss without significant overfitting, making it the preferred choice for application development. We converted this model to TensorFlow Lite to enable offline functionality on smartphones developed using Flutter. However, retrieving detailed nutritional information still requires an online database connection to ensure comprehensive nutrient data, including calories, protein, fat, and carbohydrates. This research demonstrates the potential of combining fine-tuning methods with mobile application development to promote mindful food consumption, reduce the risk of non-communicable diseases, and enhance quality of life in the digital era. Moreover, the application supports the United Nations Sustainable Development Goal 3: Good Health and Well-being by encouraging healthier lifestyle choices and contributing to improved health outcomes. Furthermore, it provides a valuable framework for the sustainable promotion of Thai food culture.